This study presented a deep learning based markerless motion capture workflow and
evaluated performance against marker-based motion capture during overground running.
Multi-view high speed (200 Hz) image data were collected concurrently with marker-based
motion capture (ground-truth data) permitting a direct comparison between methods. Lower
limb kinematic data for six participants demonstrated high levels of agreement for lower
limb joint angles with average RMSE ranging between 2.5° - 4.4° for hip sagittal and frontal
plane motion, and 4.2° - 5.2° for knee and ankle motion. These differences generally fall
within the known uncertainties of marker-based motion capture, suggesting that our
markerless approach could be used for appropriate biomechanics applications. While there
is a need for high quality open-access datasets to further facilitate performance
improvements, markerless motion capture technology continues to improve; presenting
exciting opportunities for biomechanics researchers and practitioners to capture large
amounts of high quality, ecologically valid data both in and out of the laboratory setting.
Original languageEnglish
Number of pages4
Publication statusPublished - 3 Sept 2021
EventInternational Conference on Biomechanics in Sports - Canberra, Australia
Duration: 3 Sept 20217 Sept 2021
Conference number: 39th


ConferenceInternational Conference on Biomechanics in Sports
Abbreviated titleISBS 2021
Internet address


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